1,209
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Age and gender differences in gambling intensity in a Norwegian population of electronic gaming machine players

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 92-112 | Received 07 Nov 2022, Accepted 31 Mar 2023, Published online: 19 May 2023

References

  • Abarbanel, B. L. (2014). Differences in motivational dimensions across gambling frequency, game choice and medium of play in the United Kingdom. International Gambling Studies, 14(3), 472–491. https://doi.org/10.1080/14459795.2014.966131
  • Allami, Y., Hodgins, D. C., Young, M., Brunelle, N., Currie, S., Dufour, M., Flores-Pajot, M. -C., & Nadeau, L. (2021). A meta-analysis of problem gambling risk factors in the general adult population. Addiction, 116(11), 2968–2977. https://doi.org/10.1111/add.15449
  • Ariyabuddhiphongs, V. (2012). Older adults and gambling: A review. International Journal of Mental Health and Addiction, 10(2), 297–308. https://doi.org/10.1007/s11469-011-9325-6
  • Auer, M., & Griffiths, M. D. (2014). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, 30(4), 879–887. https://doi.org/10.1007/s10899-013-9376-7
  • Auer, M., & Griffiths, M. D. (2017). Self-reported losses versus actual losses in online gambling: An empirical study. Journal of Gambling Studies, 33(3), 795–806. https://doi.org/10.1007/s10899-016-9648-0
  • Auer, M., Hopfgartner, N., & Griffiths, M. D. (2018). The effect of loss-limit reminders on gambling behavior: A real-world study of Norwegian gamblers. Journal of Behavioral Addictions, 7(4), 1056–1067. https://doi.org/10.1556/2006.7.2018.106
  • Auer, M. M., & Griffiths, M. D. (2015). The use of personalized behavioral feedback for online gamblers: An empirical study. Frontiers in Psychology, 6, 1406. https://doi.org/10.3389/fpsyg.2015.01406
  • Auer, M. M., & Griffiths, M. D. (2016). Personalized behavioral feedback for online gamblers: A real world empirical study. Frontiers in Psychology, 7, 1875. https://doi.org/10.3389/fpsyg.2016.01875
  • Baggio, S., Gainsbury, S. M., Starcevic, V., Richard, J. -B., Beck, F., & Billieux, J. (2018). Gender differences in gambling preferences and problem gambling: A network-level analysis. International Gambling Studies, 18(3), 512–525. https://doi.org/10.1080/14459795.2018.1495750
  • Baguley, T. (2009). Standardized or simple effect size: What should be reported? British Journal of Psychology, 100(3), 603–617. https://doi.org/10.1348/000712608X377117
  • Balodis, S. R. S., Thomas, A. C., & Moore, S. M. (2014). Sensitivity to reward and punishment: Horse race and EGM gamblers compared. Personality and Individual Differences, 56, 29–33. https://doi.org/10.1016/j.paid.2013.08.015
  • Botterill, E., Gill, P. R., McLaren, S., & Gomez, R. (2016). Marital status and problem gambling among Australian older adults: The mediating role of loneliness. Journal of Gambling Studies, 32(3), 1027–1038. https://doi.org/10.1007/s10899-015-9575-5
  • Braverman, J., & Shaffer, H. J. (2012). How do gamblers start gambling: Identifying behavioural markers for high-risk internet gambling. European Journal of Public Health, 22(2), 273–278. https://doi.org/10.1093/eurpub/ckp232
  • Braverman, J., Tom, M. A., & Shaffer, H. J. (2014). Accuracy of self-reported versus actual online gambling wins and losses. Psychological Assessment, 26(3), 865–877. https://doi.org/10.1037/a0036428
  • Brosowski, T., Olason, D. T., Turowski, T., & Hayer, T. (2021). The gambling consumption mediation model (GCMM): A multiple mediation approach to estimate the association of particular game types with problem gambling. Journal of Gambling Studies, 37(1), 107–140. https://doi.org/10.1007/s10899-020-09928-3
  • Browne, M., Rawat, V., Greer, N., Langham, E., Rockloff, M., & Hanley, C. (2017). What is the harm? Applying a public health methodology to measure the impact of gambling problems and harm on quality of life. Journal of Gambling Issues, 36(36), 28–50. https://doi.org/10.4309/jgi.2017.36.2
  • Browne, M., & Rockloff, M. J. (2018). Prevalence of gambling-related harm provides evidence for the prevention paradox. Journal of Behavioral Addictions, 7(2), 410–422. https://doi.org/10.1556/2006.7.2018.41
  • Calado, F., Alexandre, J., & Griffiths, M. D. (2017). Prevalence of adolescent problem gambling: A systematic review of recent research. Journal of Gambling Studies, 33(2), 397–424. https://doi.org/10.1007/s10899-016-9627-5
  • Calado, F., & Griffiths, M. D. (2016). Problem gambling worldwide: An update and systematic review of empirical research (2000–2015). Journal of Behavioral Addictions, 5(4), 592–613. https://doi.org/10.1556/2006.5.2016.073
  • Canale, N., Vieno, A., & Griffiths, M. D. (2016). The extent and distribution of gambling-related harms and the prevention paradox in a British population survey. Journal of Behavioral Addictions, 5(2), 204–212. https://doi.org/10.1556/2006.5.2016.023
  • Castrén, S., Heiskanen, M., & Salonen, A. H. (2018). Trends in gambling participation and gambling severity among finnish men and women: Cross-sectional population surveys in 2007, 2010 and 2015. BMJ Open, 8(8), e022129. https://doi.org/10.1136/bmjopen-2018-022129
  • Catania, M., & Griffiths, M. D. (2021). Understanding online voluntary self-exclusion in gambling: An empirical study using account-based behavioral tracking data. International Journal of Environmental Research and Public Health, 18(4), 2000. https://doi.org/10.3390/ijerph18042000
  • Chagas, B. T., & Gomes, J. F. S. (2017). Internet gambling: A critical review of behavioural tracking research. Journal of Gambling Issues, 36(36), 1–27. https://doi.org/10.4309/jgi.2017.36.1
  • Currie, S. R., Hodgins, D. C., Wang, J., el-Guebaly, N., Wynne, H., & Chen, S. (2006). Risk of harm among gamblers in the general population as a function of level of participation in gambling activities. Addiction, 101(4), 570–580. https://doi.org/10.1111/j.1360-0443.2006.01392.x
  • Delfabbro, P., King, D. L., & Griffiths, M. (2012). Behavioural profiling of problem gamblers: A summary and review. International Gambling Studies, 12(3), 349–366. https://doi.org/10.1080/14459795.2012.678274
  • Deng, X., Lesch, T., & Clark, L. (2019). Applying data science to behavioral analysis of online gambling. Current Addiction Reports, 6(3), 159–164. https://doi.org/10.1007/s40429-019-00269-9
  • Deng, X., Lesch, T., & Clark, L. (2021). Pareto distributions in online casino gambling: Sensitivity to timeframe and associations with self exclusion. Addictive Behaviors, 120, 106968. https://doi.org/10.1016/j.addbeh.2021.106968
  • Dragicevic, S., Tsogas, G., & Kudic, A. (2011). Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection. International Gambling Studies, 11(3), 377–391. https://doi.org/10.1080/14459795.2011.629204
  • Elton-Marshall, T., Wijesingha, R., Sendzik, T., Mock, S. E., van der Maas, M., McCready, J., Mann, R. E., & Turner, N. E. (2018). Marital status and problem gambling among older adults: An examination of social context and social motivations. Canadian Journal on Aging/La Revue canadienne du vieillissement, 37(3), 318–332. https://doi.org/10.1017/S071498081800017X
  • Fiedler, I., Kairouz, S., Costes, J. -M., & Weißmüller, K. S. (2019). Gambling spending and its concentration on problem gamblers. Journal of Business Research, 98, 82–91. https://doi.org/10.1016/j.jbusres.2019.01.040
  • Finkenwirth, S., MacDonald, K., Deng, X., Lesch, T., & Clark, L. (2021). Using machine learning to predict self-exclusion status in online gamblers on the PlayNow.Com platform in British Columbia. International Gambling Studies, 21(2), 220–237. https://doi.org/10.1080/14459795.2020.1832132
  • Gainsbury, S. M., Russell, A., Blaszczynski, A., & Hing, N. (2015). The interaction between gambling activities and modes of access: A comparison of internet-only, land-based only, and mixed-mode gamblers. Addictive Behaviors, 41, 34–40. https://doi.org/10.1016/j.addbeh.2014.09.023
  • González-Ortega, I., Echeburúa, E., Corral, P., Polo-López, R., & Alberich, S. (2013). Predictors of pathological gambling severity taking gender differences into account. European Addiction Research, 19(3), 146–154. https://doi.org/10.1159/000342311
  • Griffiths, M. (2014). The use of behavioural tracking methodologies in the study of online gambling. Sage Publications, Ltd. https://doi.org/10.4135/978144627305013517480
  • Grönroos, T., Kouvonen, A., Kontto, J., & Salonen, A. H. (2021). Socio-demographic factors, gambling behaviour, and the level of gambling expenditure: A population-based study. Journal of Gambling Studies, 38(4), 1093–1109. https://doi.org/10.1007/s10899-021-10075-6
  • Haefeli, J., Lischer, S., & Schwarz, J. (2011). Early detection items and responsible gambling features for online gambling. International Gambling Studies, 11(3), 273–288. https://doi.org/10.1080/14459795.2011.604643
  • Haeusler, J. (2016). Follow the money: Using payment behaviour as predictor for future self-exclusion. International Gambling Studies, 16(2), 246–262. https://doi.org/10.1080/14459795.2016.1158306
  • Holdsworth, L., Hing, N., & Breen, H. (2012). Exploring women’s problem gambling: A review of the literature. International Gambling Studies, 12(2), 199–213. https://doi.org/10.1080/14459795.2012.656317
  • Husky, M. M., Michel, G., Richard, J. -B., Guignard, R., & Beck, F. (2015). Gender differences in the associations of gambling activities and suicidal behaviors with problem gambling in a nationally representative French sample. Addictive Behaviors, 45, 45–50. https://doi.org/10.1016/j.addbeh.2015.01.011
  • Jonsson, J., Hodgins, D. C., Lyckberg, A., Currie, S., Young, M. M., Pallesen, S., & Carlbring, P. (2022). In search of lower risk gambling levels using behavioral data from a gambling monopolist. Journal of Behavioral Addictions, 11(3), 890–899. https://doi.org/10.1556/2006.2022.00062
  • Jonsson, J., Hodgins, D. C., Munck, I., & Carlbring, P. (2019). Reaching out to big losers: A randomized controlled trial of brief motivational contact providing gambling expenditure feedback. Psychology of Addictive Behaviors, 33(3), 179–189. https://doi.org/10.1037/adb0000447
  • Jonsson, J., Hodgins, D. C., Munck, I., & Carlbring, P. (2020). Reaching out to big losers leads to sustained reductions in gambling over 1 year: A randomized controlled trial of brief motivational contact. Addiction, 115(8), 1522–1531. https://doi.org/10.1111/add.14982
  • Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143–156. https://doi.org/10.1257/jep.15.4.143
  • Langham, E., Thorne, H., Browne, M., Donaldson, P., Rose, J., & Rockloff, M. (2016). Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health, 16(1), 80. https://doi.org/10.1186/s12889-016-2747-0
  • LaPlante, D. A., Nelson, S. E., & Gray, H. M. (2014). Breadth and depth involvement: Understanding internet gambling involvement and its relationship to gambling problems. Psychology of Addictive Behaviors, 28(2), 396–403. https://doi.org/10.1037/a0033810
  • Leino, T., Pallesen, D., Griffiths, M. D., Mentzoni, R. A., Sagoe, S., & Molde, H. (2017). Gambling behavior in alcohol-serving and non-alcohol-serving venues: A study of electronic gaming machine players using account records. Addiction Research & Theory, 25(3), 201–207. https://doi.org/10.1080/16066359.2017.1288806
  • Li, E., Browne, M., Rawat, V., Langham, E., & Rockloff, M. (2017). Breaking bad: Comparing gambling harms among gamblers and affected others. Journal of Gambling Studies, 33(1), 223–248. https://doi.org/10.1007/s10899-016-9632-8
  • Marchica, L. A., Keough, M. T., Montreuil, T. C., & Derevensky, J. L. (2020). Emotion regulation interacts with gambling motives to predict problem gambling among emerging adults. Addictive Behaviors, 106, 106378. https://doi.org/10.1016/j.addbeh.2020.106378
  • McCarthy, S., Thomas, S. L., Randle, M., Bestman, A., Pitt, H., Cowlishaw, S., & Daube, M. (2018). Women’s gambling behaviour, product preferences, and perceptions of product harm: Differences by age and gambling risk status. Harm Reduction Journal, 15(1), 22. https://doi.org/10.1186/s12954-018-0227-9
  • Merkouris, S. S., Thomas, A. C., Shandley, K. A., Rodda, S. N., Oldenhof, E., & Dowling, N. A. (2016). An update on gender differences in the characteristics associated with problem gambling: A systematic review. Current Addiction Reports, 3(3), 254–267. https://doi.org/10.1007/s40429-016-0106-y
  • Norsk Tipping. (2020) Dreams and responsibility - norsk tipping annual and social report for 2019. https://2019.norsk-tipping.no/en/
  • Orford, J. F., Griffiths, M. D., & Wardle, H. (2013). What proportion of gambling is problem gambling? Estimates from the 2010 British gambling prevalence survey. International Gambling Studies, 13(1), 4–18. https://doi.org/10.1080/14459795.2012.689001
  • Pallesen, S., Mentzoni, R. A., Morken, A. M., Engebø, J., Kaur, P., & Erevik, E. K. (2021). Changes over time and predictors of online gambling in three Norwegian population studies 2013–2019. Frontiers in Psychiatry, 12, 390. https://doi.org/10.3389/fpsyt.2021.597615
  • Pallesen, S., Pallesen, R. A., Erevik, E., Molde, H., & Morken, A. M. (2020). Omfang av penge- og dataspillproblemer i Norge 2019 [Extent of gambling and video game problems in Norway 2013]. Retrieved October 16, 2022, from https://www.uib.no/sites/w3.uib.no/files/attachments/omfang_av_penge-og_dataspillproblemer_i_norge_2019.pdf
  • Parke, A., Griffiths, M., Pattinson, J., & Keatley, D. (2018). Age-related physical and psychological vulnerability as pathways to problem gambling in older adults. Journal of Behavioral Addictions, 7(1), 137–145. https://doi.org/10.1556/2006.7.2018.18
  • R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
  • Reisel, L., Østbakken, K. M., & Attewell, P. (2019). Dynamics of claims making and gender wage gaps in the United States and Norway. Social Politics: International Studies in Gender, State & Society, 26(1), 87–115. https://doi.org/10.1093/sp/jxy019
  • Rossow, I., & Hansen, M. B. (2016). Gambling and gambling policy in Norway—An exceptional case. Addiction, 111(4), 593–598. https://doi.org/10.1111/add.13172
  • Roukka, T., & Salonen, A. H. (2020). The winners and the losers: Tax incidence of gambling in Finland. Journal of Gambling Studies, 36(4), 1183–1204. https://doi.org/10.1007/s10899-019-09899-0
  • Shaffer, H. J., & Korn, D. A. (2002). Gambling and related mental disorders: A public health analysis. Annual Review of Public Health, 23(1), 171–212. https://doi.org/10.1146/annurev.publhealth.23.100901.140532
  • Shaffer, H. J., Peller, A. J., LaPlante, D. A., Nelson, S. E., & LaBrie, R. A. (2010). Toward a paradigm shift in internet gambling research: From opinion and self-report to actual behavior. Addiction Research & Theory, 18(3), 270–283. https://doi.org/10.3109/16066350902777974
  • Statistics Norway. (2023). Income and wealth statistics for households. https://www.ssb.no/en/statbank/table/06946/tableViewLayout1/
  • Sussman, S., & Arnett, J. J. (2014). Emerging adulthood: Developmental period facilitative of the addictions. Evaluation & the Health Professions, 37(2), 147–155. https://doi.org/10.1177/0163278714521812
  • Tse, S., Hong, S. -I., Wang, C. -W., & Cunningham-Williams, R. M. (2012). Gambling behavior and problems among older adults: A systematic review of empirical studies. The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences, 67(5), 639–652. https://doi.org/10.1093/geronb/gbs068
  • Ukhov, I., Bjurgert, J., Auer, M., & Griffiths, M. D. (2021). Online problem gambling: A comparison of casino players and sports bettors via predictive modeling using behavioral tracking data. Journal of Gambling Studies, 37(3), 877–897. https://doi.org/10.1007/s10899-020-09964-z
  • Venne, D., Mazar, A., & Volberg, R. (2020). Gender and gambling behaviors: A comprehensive analysis of (dis)similarities. International Journal of Mental Health and Addiction, 18(5), 1181–1195. https://doi.org/10.1007/s11469-019-00116-y
  • Volberg, R. A., Gupta, R. S., Griffiths, M. D., Olason, D. T., & Delfabbro, P. H. (2010). An international perspective on youth gambling prevalence studies. International Journal of Adolescent Medicine and Health. https://doi.org/10.1515/IJAMH.2010.22.1.3
  • Welte, J. W., Barnes, G. M., Tidwell, M. -C.O., & Wieczorek, W. F. (2017). Predictors of problem gambling in the U.S. Journal of Gambling Studies, 33(2), 327–342. https://doi.org/10.1007/s10899-016-9639-1
  • Whiteford, S., Hoon, A. E., James, R., Tunney, R., & Dymond, S. (2022). Quantile regression analysis of in-play betting in a large online gambling dataset. Computers in Human Behavior Reports, 6, 100194. https://doi.org/10.1016/j.chbr.2022.100194
  • Wood, R. T. A., & Griffiths, M. D. (2007). A qualitative investigation of problem gambling as an escape-based coping strategy. Psychology and Psychotherapy: Theory, Research and Practice, 80, 107–125.